Articles with public access mandates - Joseph LedsamLearn more
Available somewhere: 18
International evaluation of an AI system for breast cancer screening
SM McKinney, M Sieniek, V Godbole, J Godwin, N Antropova, H Ashrafian, ...
Nature 577 (7788), 89-94, 2020
Mandates: Cancer Research UK, National Institute for Health Research, UK
Clinically applicable deep learning for diagnosis and referral in retinal disease
J De Fauw, JR Ledsam, B Romera-Paredes, S Nikolov, N Tomasev, ...
Nature medicine 24 (9), 1342-1350, 2018
Mandates: National Institute for Health Research, UK, Wellcome Trust
A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis
X Liu, L Faes, AU Kale, SK Wagner, DJ Fu, A Bruynseels, T Mahendiran, ...
The lancet digital health 1 (6), e271-e297, 2019
Mandates: UK Medical Research Council, National Institute for Health Research, UK …
A clinically applicable approach to continuous prediction of future acute kidney injury
N Tomašev, X Glorot, JW Rae, M Zielinski, H Askham, A Saraiva, ...
Nature 572 (7767), 116-119, 2019
Mandates: US Department of Veterans Affairs, National Institute for Health Research, UK
Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study
L Faes, SK Wagner, DJ Fu, X Liu, E Korot, JR Ledsam, T Back, R Chopra, ...
The Lancet Digital Health 1 (5), e232-e242, 2019
Mandates: UK Medical Research Council, National Institute for Health Research, UK
Predicting conversion to wet age-related macular degeneration using deep learning
J Yim, R Chopra, T Spitz, J Winkens, A Obika, C Kelly, H Askham, M Lukic, ...
Nature Medicine 26 (6), 892-899, 2020
Mandates: UK Medical Research Council, National Institute for Health Research, UK
Clinically applicable segmentation of head and neck anatomy for radiotherapy: deep learning algorithm development and validation study
S Nikolov, S Blackwell, A Zverovitch, R Mendes, M Livne, J De Fauw, ...
Journal of medical Internet research 23 (7), e26151, 2021
Mandates: National Institute for Health Research, UK
Rapid advances in auto-segmentation of organs at risk and target volumes in head and neck cancer
M Kosmin, J Ledsam, B Romera-Paredes, R Mendes, S Moinuddin, ...
Radiotherapy and Oncology 135, 130-140, 2019
Mandates: Cancer Research UK, National Institute for Health Research, UK
Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning
AV Varadarajan, P Bavishi, P Ruamviboonsuk, P Chotcomwongse, ...
Nature communications 11 (1), 130, 2020
Mandates: National Institute for Health Research, UK
Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records
N Tomašev, N Harris, S Baur, A Mottram, X Glorot, JW Rae, M Zielinski, ...
Nature Protocols 16 (6), 2765-2787, 2021
Mandates: National Institute for Health Research, UK
Automated analysis of retinal imaging using machine learning techniques for computer vision
J De Fauw, P Keane, N Tomasev, D Visentin, G van den Driessche, ...
F1000Research 5, 2016
Mandates: National Institute for Health Research, UK
Service evaluation of the implementation of a digitally-enabled care pathway for the recognition and management of acute kidney injury
A Connell, H Montgomery, S Morris, C Nightingale, S Stanley, M Emerson, ...
F1000Research 6, 2017
Mandates: National Institute for Health Research, UK
DCE-MRI for early prediction of response in hepatocellular carcinoma after TACE and sorafenib therapy: a pilot study
K Saito, J Ledsam, K Sugimoto, S Sourbron, Y Araki, K Tokuuye
Journal of the Belgian Society of Radiology 102 (1), 2018
Mandates: UK Medical Research Council
Validation study of perfusion parameter in hypervascular hepatocellular carcinoma and focal nodular hyperplasia using dynamic susceptibility magnetic resonance imaging with …
K Saito, J Ledsam, S Sourbron, Y Araki
Quantitative Imaging in Medicine and Surgery 10 (6), 1298, 2020
Mandates: UK Medical Research Council
Developing Deep Learning Continuous Risk Models for Early Adverse Event Prediction in Electronic Health Records: an AKI Case Study
N Tomašev, X Glorot, JW Rae, M Zielinski, H Askham, A Saraiva, ...
Mandates: US Department of Veterans Affairs, National Institute for Health Research, UK
kidney injury [version 1; referees: 2 approved]
A Connell, H Montgomery, S Morris, C Nightingale, S Stanley, M Emerson, ...
Mandates: National Institute for Health Research, UK
digitally-enabled care pathway for the recognition and management of acute kidney injury [version 2; referees: 2
A Connell, H Montgomery, S Morris, C Nightingale, S Stanley, M Emerson, ...
Mandates: National Institute for Health Research, UK
learning techniques for computer vision [version 2; referees: 2
J De Fauw, P Keane, N Tomasev, D Visentin, G van den Driessche, ...
Mandates: National Institute for Health Research, UK
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